Bibliographic record
Abstract
“3 Stories from the Chthulucene” attempts to work through some of the theoretical framework outlined in Donna Haraway’s Staying with the Trouble: Making Kin in the Chthulucene (2016). The term “Chthulucene” derives from H. P. Lovecraft’s science-fictional tentacled being, the Cthulhu. Haraway theorizes the Chthulucene as an alternative to dominant notions of our time as the Anthropocene. Rather than seeing humans as separate from the environment and acting on it, Chthulucene theorists see humans as part of an intricate web of relations between human and more-than-human life, where humans act through the environment. The methodology used in this article is research-creation, specifically creation as research. This article begins with three autofictional vignettes that aim to play with research questions such as: How might Haraway’s concept of “tentacular thinking" impact the way narrative is structured? Is there an accessible way to write Haraway’s theories so that they are approachable but still “writerly"? How can writer and reader together (re)imagine utopias, in particular queer utopias, so that they are still situated strongly in the present? A supplementary essay follows these three vignettes and attempts to tease out some of these findings within a more typically academic format.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".